HearQA for Discovery Calls — Founder + PM Customer-Discovery Conversations
Real-time AI coaching for founder customer interviews, PM problem discovery, and user-research calls — Mom-Test framework prompts, JTBD recall, and uncommitted-question surfacing without breaking eye contact.
Get Started FreeCommon Challenges
Discovery calls where the customer was telling you what you wanted to hear ("yes, we'd buy that") and you needed The Mom Test's pivot questions to break through the politeness — but couldn't recall them in the moment
User-research interviews where you needed the next JTBD question ("when did you last hire a tool to do this job?") but ended up asking a leading question because that was easier to remember
Founder customer-discovery sessions where you'd done the prep but lost the thread mid-conversation — the customer raised a use case adjacent to your hypothesis and you didn't know whether to follow it or steer back
Cross-cultural interviews where you couldn't fluently follow up in the customer's preferred language without sounding awkward, costing you the depth of response you needed
Note-taking-vs-listening tradeoff: typing the customer's exact words meant you missed the next signal; not typing them meant your post-interview synthesis was approximate, not specific
How HearQA Helps
Mom-Test discipline in real time
Upload Rob Fitzpatrick's The Mom Test framework, your problem-discovery interview template, and your specific hypothesis docs. HearQA surfaces the right pivot question when the customer's answer is too positive to be useful — "can you tell me about the last time you had this problem?" instead of "would you buy this?" The framework is hard to internalize cold; having it surface live as the conversation drifts toward leading questions keeps the interview honest.
JTBD framework recall
Christensen's Jobs-To-Be-Done framework has a specific question shape — "when you last hired a tool to do this job, what did the day look like?" — that produces dramatically better answers than "what do you want?" Upload your JTBD interview templates, your hypothesis docs, and your customer-segment definitions; HearQA surfaces the right JTBD prompt as the conversation moves through context-setting, force-progress mapping, and competitor-substitute identification.
Hypothesis tracking across the conversation
Before the call, upload your interview hypothesis docs (the question you're trying to answer, the falsification criteria you've set, the disconfirming evidence you'd accept). During the call, HearQA's transcription captures both sides; the AI surfaces moments when the customer's answer disconfirms your hypothesis — exactly the signal a founder is most prone to dismissing in real time. After the call, the session summary highlights the disconfirmation moments with timestamps for review.
Multi-language depth without losing fluency
International discovery calls — selling US software into LATAM SMB, doing user research with Japanese enterprise buyers, interviewing Indian developers — work best in the customer's preferred language. HearQA supports 8 locales (en, pt, es, fr, de, hi, ja, ko); ask follow-up questions in the customer's language with framework prompts surfaced for you, capture the response with full transcription, and synthesize in your working language post-call.
Practice → Mock Interview for hard discovery types
Before a high-stakes discovery call — your first call with an ICP segment, a board-mandated customer-validation sprint, the discovery interview that determines whether to pivot — run Practice → Mock Interview with the AI playing the customer persona you're about to talk to. Upload the customer brief and your hypothesis docs; the AI generates customer-typical responses (vague positivity, deflecting concerns, leading-question fishing) so you rehearse the pivot questions. By the live call, the framework is muscle memory.
Key Features for Discovery Calls
- Real-time tab-audio capture for Zoom, Google Meet, and Microsoft Teams (Chrome desktop) — full bidirectional transcription so the post-call synthesis is the customer's actual words, not your approximation
- Document RAG over Mom-Test framework, JTBD templates, your hypothesis docs, customer-segment definitions, prior interview transcripts
- Practice → Mock Interview sub-type with AI playing customer personas raising the leading questions and vague positivity that real interviews surface
- Per-call session summary with auto-extracted disconfirmation moments, JTBD-pattern signals, and the customer's actual quotes (timestamped)
- Multi-language transcription + framework-prompt support across 8 locales for international discovery work
- No detection concern: discovery calls are between you and the person you're trying to learn from; there is no proctoring stack
- Free / Session Pack tier suitable for founder customer-discovery sprints (one-week intensive interview blocks)
- Session history with searchable transcripts — find every customer who mentioned a specific pain point across 50+ interviews in seconds
“I was running a customer-validation sprint for our Series A fundraise — 30 interviews in two weeks across three ICP segments. HearQA surfaced the Mom-Test pivot questions in real time and the JTBD prompts when I was drifting toward leading-question fishing. The post-call session summaries flagged 3 interviews where the customer's answer disconfirmed my hypothesis — exactly the signal I'd have rationalized away if I were synthesizing from notes 4 hours later. We pivoted the positioning before the second investor pitch.”
Founder, B2B SaaS pre-Series A
Quick Pricing Overview
Free
$0
3 sessions/month
Pro (Monthly)
$89.99
25 sessions/month
Session Pack
$44.99
5 sessions, one-time
Pro (Annual)
$599.99
Save 44%
Frequently Asked Questions
How is this different from Dovetail, Notably, or other research-repository tools?
Dovetail and the research-repository category are post-interview synthesis tools — they organize, tag, and surface patterns across interview transcripts AFTER the conversation. HearQA is in-call coaching — it tells you what to ask DURING the conversation. Different problem class. Most user-research teams using both: Dovetail for cross-interview synthesis and stakeholder-shareable artifacts, HearQA for the live moment when a researcher needs to recall the right Mom-Test pivot question or JTBD prompt while the customer is mid-sentence. They're complements; if you can only afford one, the right choice depends on your stage. Pre-PMF founders benefit more from in-call coaching (HearQA); 5+ researcher orgs benefit more from cross-interview synthesis at scale (Dovetail).
Is HearQA detectable by Zoom, Google Meet, or Microsoft Teams during a discovery call?
No — and there's no incentive for these platforms to add detection. Discovery calls are between two consenting parties trying to have a useful conversation; the platforms are actively integrating AI INTO calls (Zoom AI Companion, Meet Gemini, Teams Copilot). The phone-off-camera setup is unremarkable rather than technically concealed — the customer doesn't know HearQA exists, and even if they did, they'd just want it for themselves.
Will the customer notice me reading from a phone during a discovery interview?
Less than you'd think — the phone is off-camera, you glance at it briefly between questions, and the customer is focused on telling their story. The bigger risk is overusing it: if you read the framework prompts verbatim, the conversation feels scripted and the customer sense-makes around your script instead of telling you what they actually do. The pattern that works: glance to internalize the framework, look back at the camera, ask the question in your own words. HearQA is a thought-partner for the framework, not a script for the question.
How does Practice → Mock Interview work for discovery prep?
Pick the Practice template, sub-type "Mock Interview", and configure for customer-discovery (rather than the default job-interview behavior). Upload the customer brief, your hypothesis docs, and the framework you're using (Mom-Test / JTBD / Lean Customer Development). The AI plays the customer — vague positivity, deflecting concerns, leading-question fishing patterns that real customer interviews surface. You ask discovery questions; the AI scores your handling on Mom-Test discipline (avoided leading questions), pivot-question recall (broke through the positivity), and JTBD-pattern adherence (asked the force-progress / context-setting / competitor-substitute questions). Run 3 sessions; the live call becomes the fourth.
What documents should I upload for a discovery sprint?
Three buckets work best. (1) Frameworks: The Mom Test summary, JTBD interview template, your specific Lean Customer Development variant. (2) Hypothesis docs: the question you're trying to answer, the falsification criteria you've set in advance (so you can recognize disconfirming evidence in the moment), the customer-segment definition for this batch. (3) Prior-interview pattern docs: if you've done 5+ prior interviews and have a working pattern hypothesis, upload the synthesis so HearQA can flag when a new interview confirms or breaks the pattern. The hypothesis-tracking value is highest when you've articulated what you're testing in writing first.
What's the right HearQA pricing tier for a customer-discovery sprint?
Session Pack ($44.99 one-time, 5 sessions over 7 days) is built for exactly this — a founder doing 5–10 customer-discovery interviews in a week. Run all 5 Practice rehearsals before the live calls, use the live AI in the actual interviews. Pro at $89/mo makes sense if customer discovery is an ongoing weekly cadence (research-track founders, PMs in continuous-discovery mode); the Free tier (3 sessions/month) is enough for an occasional interview but not a sprint.
Ready to try HearQA?
Sign up free and start your first session today.
HearQA opens to everyone in a few days — we'll email you the moment it's live.